Overview

Dataset statistics

Number of variables16
Number of observations1118122
Missing cells194456
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.5 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 45406 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
a is highly correlated with sHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 48614 (4.3%) missing values Missing
jerseyNumber has 48614 (4.3%) missing values Missing
o has 48614 (4.3%) missing values Missing
dir has 48614 (4.3%) missing values Missing
s has 71193 (6.4%) zeros Zeros
a has 66378 (5.9%) zeros Zeros
dis has 70153 (6.3%) zeros Zeros

Reproduction

Analysis started2022-11-02 15:07:08.588089
Analysis finished2022-11-02 15:08:47.232478
Duration1 minute and 38.64 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021091189
Minimum2021090900
Maximum2021091300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:47.279718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021090900
5-th percentile2021090900
Q12021091202
median2021091206
Q32021091210
95-th percentile2021091300
Maximum2021091300
Range400
Interquartile range (IQR)8

Descriptive statistics

Standard deviation90.53526954
Coefficient of variation (CV)4.479524232 × 10-8
Kurtosis5.740632478
Mean2021091189
Median Absolute Deviation (MAD)4
Skewness-2.521368112
Sum2.259826522 × 1015
Variance8196.635031
MonotonicityIncreasing
2022-11-02T12:08:47.374443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202109090092644
 
8.3%
202109130092299
 
8.3%
202109120482271
 
7.4%
202109120176406
 
6.8%
202109120575739
 
6.8%
202109121272266
 
6.5%
202109120371116
 
6.4%
202109120069299
 
6.2%
202109120268172
 
6.1%
202109120965274
 
5.8%
Other values (6)352636
31.5%
ValueCountFrequency (%)
202109090092644
8.3%
202109120069299
6.2%
202109120176406
6.8%
202109120268172
6.1%
202109120371116
6.4%
202109120482271
7.4%
202109120575739
6.8%
202109120663917
5.7%
202109120763848
5.7%
202109120864653
5.8%
ValueCountFrequency (%)
202109130092299
8.3%
202109121357799
5.2%
202109121272266
6.5%
202109121148323
4.3%
202109121054096
4.8%
202109120965274
5.8%
202109120864653
5.8%
202109120763848
5.7%
202109120663917
5.7%
202109120575739
6.8%

playId
Real number (ℝ≥0)

Distinct1021
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2187.559921
Minimum55
Maximum4849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:47.492962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile262
Q11166
median2145
Q33195
95-th percentile4233
Maximum4849
Range4794
Interquartile range (IQR)2029

Descriptive statistics

Standard deviation1243.918429
Coefficient of variation (CV)0.5686328485
Kurtosis-1.009917699
Mean2187.559921
Median Absolute Deviation (MAD)1017
Skewness0.1122809555
Sum2445958874
Variance1547333.058
MonotonicityNot monotonic
2022-11-02T12:08:47.622816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20634991
 
0.4%
34064968
 
0.4%
21294669
 
0.4%
19383312
 
0.3%
6203289
 
0.3%
19883220
 
0.3%
7883220
 
0.3%
763174
 
0.3%
4213013
 
0.3%
22192944
 
0.3%
Other values (1011)1081322
96.7%
ValueCountFrequency (%)
55713
 
0.1%
561702
0.2%
63736
 
0.1%
69736
 
0.1%
763174
0.3%
771150
 
0.1%
78713
 
0.1%
84644
 
0.1%
85713
 
0.1%
88644
 
0.1%
ValueCountFrequency (%)
48491035
0.1%
4845782
0.1%
4772667
0.1%
47651081
0.1%
4750805
0.1%
4736805
0.1%
4728690
0.1%
4699874
0.1%
4695713
0.1%
4691966
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1162
Distinct (%)0.1%
Missing48614
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45460.32293
Minimum25511
Maximum53957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:47.750664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37104
Q142404
median44999
Q347917
95-th percentile53462
Maximum53957
Range28446
Interquartile range (IQR)5513

Descriptive statistics

Standard deviation4938.338482
Coefficient of variation (CV)0.1086296393
Kurtosis0.02710664664
Mean45460.32293
Median Absolute Deviation (MAD)2835
Skewness-0.1592549538
Sum4.862017906 × 1010
Variance24387186.96
MonotonicityNot monotonic
2022-11-02T12:08:47.873142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460892700
 
0.2%
434532700
 
0.2%
534362700
 
0.2%
524832700
 
0.2%
484552700
 
0.2%
432902700
 
0.2%
536012672
 
0.2%
461872440
 
0.2%
460842440
 
0.2%
525172440
 
0.2%
Other values (1152)1043316
93.3%
(Missing)48614
 
4.3%
ValueCountFrequency (%)
255111690
0.2%
289631055
0.1%
29550502
 
< 0.1%
298511071
0.1%
30078334
 
< 0.1%
30842249
 
< 0.1%
308691007
0.1%
330841728
0.2%
331071033
0.1%
33130390
 
< 0.1%
ValueCountFrequency (%)
53957321
 
< 0.1%
53946177
 
< 0.1%
5393568
 
< 0.1%
53930444
 
< 0.1%
53876134
 
< 0.1%
5368747
 
< 0.1%
53685159
 
< 0.1%
53679125
 
< 0.1%
536741963
0.2%
53668489
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct177
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.00878348
Minimum1
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:48.007245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q332
95-th percentile50
Maximum177
Range176
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.12365472
Coefficient of variation (CV)0.7007608524
Kurtosis7.282689269
Mean23.00878348
Median Absolute Deviation (MAD)10
Skewness1.644070527
Sum25726627
Variance259.9722416
MonotonicityNot monotonic
2022-11-02T12:08:48.130995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127025
 
2.4%
227025
 
2.4%
2127025
 
2.4%
2027025
 
2.4%
1927025
 
2.4%
1827025
 
2.4%
1727025
 
2.4%
1627025
 
2.4%
1527025
 
2.4%
1427025
 
2.4%
Other values (167)847872
75.8%
ValueCountFrequency (%)
127025
2.4%
227025
2.4%
327025
2.4%
427025
2.4%
527025
2.4%
627025
2.4%
727025
2.4%
827025
2.4%
927025
2.4%
1027025
2.4%
ValueCountFrequency (%)
17723
< 0.1%
17623
< 0.1%
17523
< 0.1%
17423
< 0.1%
17323
< 0.1%
17223
< 0.1%
17123
< 0.1%
17023
< 0.1%
16923
< 0.1%
16823
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct45406
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2021-09-12T18:04:04.900
 
69
2021-09-12T19:59:55.500
 
69
2021-09-12T19:59:55.300
 
69
2021-09-12T19:59:55.200
 
69
2021-09-12T22:46:40.400
 
69
Other values (45401)
1117777 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters25716806
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2021-09-10T00:26:31.100
2nd row2021-09-10T00:26:31.200
3rd row2021-09-10T00:26:31.300
4th row2021-09-10T00:26:31.400
5th row2021-09-10T00:26:31.500

Common Values

ValueCountFrequency (%)
2021-09-12T18:04:04.90069
 
< 0.1%
2021-09-12T19:59:55.50069
 
< 0.1%
2021-09-12T19:59:55.30069
 
< 0.1%
2021-09-12T19:59:55.20069
 
< 0.1%
2021-09-12T22:46:40.40069
 
< 0.1%
2021-09-12T22:46:40.30069
 
< 0.1%
2021-09-12T17:53:10.90069
 
< 0.1%
2021-09-12T17:53:10.80069
 
< 0.1%
2021-09-12T18:59:07.90069
 
< 0.1%
2021-09-12T18:59:08.00069
 
< 0.1%
Other values (45396)1117432
99.9%

Length

2022-11-02T12:08:48.246415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-09-12t18:04:04.90069
 
< 0.1%
2021-09-12t17:28:55.00069
 
< 0.1%
2021-09-12t17:28:54.80069
 
< 0.1%
2021-09-12t17:28:54.60069
 
< 0.1%
2021-09-12t17:28:54.40069
 
< 0.1%
2021-09-12t17:28:53.20069
 
< 0.1%
2021-09-12t17:28:54.30069
 
< 0.1%
2021-09-12t17:28:54.20069
 
< 0.1%
2021-09-12t17:28:54.10069
 
< 0.1%
2021-09-12t17:28:54.00069
 
< 0.1%
Other values (45396)1117432
99.9%

Most occurring characters

ValueCountFrequency (%)
05732773
22.3%
24323516
16.8%
13641593
14.2%
-2236244
 
8.7%
:2236244
 
8.7%
91664602
 
6.5%
T1118122
 
4.3%
.1118122
 
4.3%
3812383
 
3.2%
4797824
 
3.1%
Other values (4)2035383
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number19008074
73.9%
Other Punctuation3354366
 
13.0%
Dash Punctuation2236244
 
8.7%
Uppercase Letter1118122
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05732773
30.2%
24323516
22.7%
13641593
19.2%
91664602
 
8.8%
3812383
 
4.3%
4797824
 
4.2%
5695036
 
3.7%
8516764
 
2.7%
7492453
 
2.6%
6331130
 
1.7%
Other Punctuation
ValueCountFrequency (%)
:2236244
66.7%
.1118122
33.3%
Dash Punctuation
ValueCountFrequency (%)
-2236244
100.0%
Uppercase Letter
ValueCountFrequency (%)
T1118122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24598684
95.7%
Latin1118122
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
05732773
23.3%
24323516
17.6%
13641593
14.8%
-2236244
 
9.1%
:2236244
 
9.1%
91664602
 
6.8%
.1118122
 
4.5%
3812383
 
3.3%
4797824
 
3.2%
5695036
 
2.8%
Other values (3)1340347
 
5.4%
Latin
ValueCountFrequency (%)
T1118122
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25716806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05732773
22.3%
24323516
16.8%
13641593
14.2%
-2236244
 
8.7%
:2236244
 
8.7%
91664602
 
6.5%
T1118122
 
4.3%
.1118122
 
4.3%
3812383
 
3.2%
4797824
 
3.1%
Other values (4)2035383
 
7.9%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)< 0.1%
Missing48614
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean49.46293716
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:48.541327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q122
median52
Q375
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation30.02460577
Coefficient of variation (CV)0.6070121891
Kurtosis-1.347424287
Mean49.46293716
Median Absolute Deviation (MAD)27
Skewness0.03076410867
Sum52901007
Variance901.4769514
MonotonicityNot monotonic
2022-11-02T12:08:48.669349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2327032
 
2.4%
2121718
 
1.9%
1121176
 
1.9%
9020588
 
1.8%
2420328
 
1.8%
7619610
 
1.8%
7419316
 
1.7%
219227
 
1.7%
2618104
 
1.6%
7217986
 
1.6%
Other values (89)864423
77.3%
(Missing)48614
 
4.3%
ValueCountFrequency (%)
112809
1.1%
219227
1.7%
37991
0.7%
411779
1.1%
57551
 
0.7%
611202
1.0%
79156
0.8%
814850
1.3%
96177
 
0.6%
1014182
1.3%
ValueCountFrequency (%)
9912525
1.1%
9813925
1.2%
9716611
1.5%
969809
0.9%
9510636
1.0%
9416522
1.5%
9312138
1.1%
926339
 
0.6%
9114946
1.3%
9020588
1.8%

team
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
football
 
48614
TB
 
44308
DAL
 
44308
BAL
 
44143
LV
 
44143
Other values (28)
892606 

Length

Max length8
Median length3
Mean length2.985973803
Min length2

Characters and Unicode

Total characters3338683
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowTB
3rd rowTB
4th rowTB
5th rowTB

Common Values

ValueCountFrequency (%)
football48614
 
4.3%
TB44308
 
4.0%
DAL44308
 
4.0%
BAL44143
 
3.9%
LV44143
 
3.9%
SF39347
 
3.5%
DET39347
 
3.5%
PIT36542
 
3.3%
BUF36542
 
3.3%
HOU36223
 
3.2%
Other values (23)704605
63.0%

Length

2022-11-02T12:08:48.783911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football48614
 
4.3%
tb44308
 
4.0%
dal44308
 
4.0%
bal44143
 
3.9%
lv44143
 
3.9%
sf39347
 
3.5%
det39347
 
3.5%
pit36542
 
3.3%
buf36542
 
3.3%
hou36223
 
3.2%
Other values (23)704605
63.0%

Most occurring characters

ValueCountFrequency (%)
A366883
 
11.0%
N279840
 
8.4%
L255519
 
7.7%
I252329
 
7.6%
E192104
 
5.8%
C187616
 
5.6%
T183876
 
5.5%
D148786
 
4.5%
B148104
 
4.4%
S100837
 
3.0%
Other values (20)1222789
36.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2949771
88.4%
Lowercase Letter388912
 
11.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A366883
12.4%
N279840
 
9.5%
L255519
 
8.7%
I252329
 
8.6%
E192104
 
6.5%
C187616
 
6.4%
T183876
 
6.2%
D148786
 
5.0%
B148104
 
5.0%
S100837
 
3.4%
Other values (14)833877
28.3%
Lowercase Letter
ValueCountFrequency (%)
l97228
25.0%
o97228
25.0%
f48614
12.5%
a48614
12.5%
b48614
12.5%
t48614
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3338683
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A366883
 
11.0%
N279840
 
8.4%
L255519
 
7.7%
I252329
 
7.6%
E192104
 
5.8%
C187616
 
5.6%
T183876
 
5.5%
D148786
 
4.5%
B148104
 
4.4%
S100837
 
3.0%
Other values (20)1222789
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3338683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A366883
 
11.0%
N279840
 
8.4%
L255519
 
7.7%
I252329
 
7.6%
E192104
 
5.8%
C187616
 
5.6%
T183876
 
5.5%
D148786
 
4.5%
B148104
 
4.4%
S100837
 
3.0%
Other values (20)1222789
36.6%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
right
568031 
left
550091 

Length

Max length5
Median length5
Mean length4.50802238
Min length4

Characters and Unicode

Total characters5040519
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
right568031
50.8%
left550091
49.2%

Length

2022-11-02T12:08:48.877917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T12:08:48.971197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
right568031
50.8%
left550091
49.2%

Most occurring characters

ValueCountFrequency (%)
t1118122
22.2%
r568031
11.3%
i568031
11.3%
g568031
11.3%
h568031
11.3%
l550091
10.9%
e550091
10.9%
f550091
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5040519
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1118122
22.2%
r568031
11.3%
i568031
11.3%
g568031
11.3%
h568031
11.3%
l550091
10.9%
e550091
10.9%
f550091
10.9%

Most occurring scripts

ValueCountFrequency (%)
Latin5040519
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1118122
22.2%
r568031
11.3%
i568031
11.3%
g568031
11.3%
h568031
11.3%
l550091
10.9%
e550091
10.9%
f550091
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5040519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1118122
22.2%
r568031
11.3%
i568031
11.3%
g568031
11.3%
h568031
11.3%
l550091
10.9%
e550091
10.9%
f550091
10.9%

x
Real number (ℝ≥0)

Distinct11708
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.95227535
Minimum0.25
Maximum119.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:49.066040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile20.72
Q140.8
median60.13
Q378.87
95-th percentile99.6195
Maximum119.72
Range119.47
Interquartile range (IQR)38.07

Descriptive statistics

Standard deviation24.22567114
Coefficient of variation (CV)0.4040825973
Kurtosis-0.8226969423
Mean59.95227535
Median Absolute Deviation (MAD)19.04
Skewness0.009726454646
Sum67033958.02
Variance586.8831421
MonotonicityNot monotonic
2022-11-02T12:08:49.197536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.62203
 
< 0.1%
86.45203
 
< 0.1%
66.16200
 
< 0.1%
86.55200
 
< 0.1%
64.38200
 
< 0.1%
66.76198
 
< 0.1%
61.83198
 
< 0.1%
67.19197
 
< 0.1%
64.39197
 
< 0.1%
61.29196
 
< 0.1%
Other values (11698)1116130
99.8%
ValueCountFrequency (%)
0.252
< 0.1%
0.262
< 0.1%
0.271
< 0.1%
0.281
< 0.1%
0.291
< 0.1%
0.311
< 0.1%
0.341
< 0.1%
0.381
< 0.1%
0.421
< 0.1%
0.491
< 0.1%
ValueCountFrequency (%)
119.721
< 0.1%
119.711
< 0.1%
119.681
< 0.1%
119.641
< 0.1%
119.61
< 0.1%
119.591
< 0.1%
119.522
< 0.1%
119.471
< 0.1%
119.421
< 0.1%
119.351
< 0.1%

y
Real number (ℝ)

Distinct5386
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.84171764
Minimum-2.61
Maximum57.01
Zeros1
Zeros (%)< 0.1%
Negative58
Negative (%)< 0.1%
Memory size8.5 MiB
2022-11-02T12:08:49.328699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.61
5-th percentile11.39
Q122.03
median26.85
Q331.71
95-th percentile42.16
Maximum57.01
Range59.62
Interquartile range (IQR)9.68

Descriptive statistics

Standard deviation8.385431814
Coefficient of variation (CV)0.3124029515
Kurtosis0.2898441369
Mean26.84171764
Median Absolute Deviation (MAD)4.84
Skewness-0.01626457147
Sum30012315.01
Variance70.3154667
MonotonicityNot monotonic
2022-11-02T12:08:49.448794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.741085
 
0.1%
23.751062
 
0.1%
23.81058
 
0.1%
23.91049
 
0.1%
29.91039
 
0.1%
23.881038
 
0.1%
29.881034
 
0.1%
23.831032
 
0.1%
23.871031
 
0.1%
23.851030
 
0.1%
Other values (5376)1107664
99.1%
ValueCountFrequency (%)
-2.619
< 0.1%
-2.63
 
< 0.1%
-2.592
 
< 0.1%
-2.583
 
< 0.1%
-2.571
 
< 0.1%
-2.561
 
< 0.1%
-2.543
 
< 0.1%
-2.533
 
< 0.1%
-2.523
 
< 0.1%
-2.515
< 0.1%
ValueCountFrequency (%)
57.011
< 0.1%
56.471
< 0.1%
56.441
< 0.1%
55.851
< 0.1%
55.821
< 0.1%
55.811
< 0.1%
55.711
< 0.1%
55.622
< 0.1%
55.61
< 0.1%
55.591
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2178
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.574296061
Minimum0
Maximum28.3
Zeros71193
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:49.577039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.73
median2.13
Q33.8
95-th percentile6.75
Maximum28.3
Range28.3
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.403109625
Coefficient of variation (CV)0.9335016518
Kurtosis15.28627553
Mean2.574296061
Median Absolute Deviation (MAD)1.51
Skewness2.441052427
Sum2878377.06
Variance5.774935869
MonotonicityNot monotonic
2022-11-02T12:08:49.693356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071193
 
6.4%
0.0118089
 
1.6%
0.0210548
 
0.9%
0.037660
 
0.7%
0.046304
 
0.6%
0.055545
 
0.5%
0.064823
 
0.4%
0.074679
 
0.4%
0.084283
 
0.4%
0.094042
 
0.4%
Other values (2168)980956
87.7%
ValueCountFrequency (%)
071193
6.4%
0.0118089
 
1.6%
0.0210548
 
0.9%
0.037660
 
0.7%
0.046304
 
0.6%
0.055545
 
0.5%
0.064823
 
0.4%
0.074679
 
0.4%
0.084283
 
0.4%
0.094042
 
0.4%
ValueCountFrequency (%)
28.31
< 0.1%
28.211
< 0.1%
28.181
< 0.1%
28.041
< 0.1%
28.011
< 0.1%
27.961
< 0.1%
27.81
< 0.1%
27.761
< 0.1%
27.711
< 0.1%
27.611
< 0.1%

a
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1659
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.794617904
Minimum0
Maximum50.69
Zeros66378
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:49.819002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.7
median1.54
Q32.59
95-th percentile4.47
Maximum50.69
Range50.69
Interquartile range (IQR)1.89

Descriptive statistics

Standard deviation1.458680584
Coefficient of variation (CV)0.812808443
Kurtosis9.463408966
Mean1.794617904
Median Absolute Deviation (MAD)0.93
Skewness1.600224501
Sum2006601.76
Variance2.127749047
MonotonicityNot monotonic
2022-11-02T12:08:49.948746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066378
 
5.9%
0.0114490
 
1.3%
0.028486
 
0.8%
0.036263
 
0.6%
0.045177
 
0.5%
0.054413
 
0.4%
0.063711
 
0.3%
1.013533
 
0.3%
1.293502
 
0.3%
1.13474
 
0.3%
Other values (1649)998695
89.3%
ValueCountFrequency (%)
066378
5.9%
0.0114490
 
1.3%
0.028486
 
0.8%
0.036263
 
0.6%
0.045177
 
0.5%
0.054413
 
0.4%
0.063711
 
0.3%
0.073393
 
0.3%
0.083012
 
0.3%
0.092837
 
0.3%
ValueCountFrequency (%)
50.691
< 0.1%
36.391
< 0.1%
31.551
< 0.1%
28.871
< 0.1%
28.771
< 0.1%
28.481
< 0.1%
26.671
< 0.1%
26.21
< 0.1%
26.161
< 0.1%
25.881
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct563
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2608324762
Minimum0
Maximum8.46
Zeros70153
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:50.079703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07
median0.21
Q30.38
95-th percentile0.68
Maximum8.46
Range8.46
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.2579967705
Coefficient of variation (CV)0.9891282492
Kurtosis54.10544083
Mean0.2608324762
Median Absolute Deviation (MAD)0.15
Skewness4.408429249
Sum291642.53
Variance0.06656233361
MonotonicityNot monotonic
2022-11-02T12:08:50.197589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070153
 
6.3%
0.0162703
 
5.6%
0.0235547
 
3.2%
0.0327544
 
2.5%
0.0423568
 
2.1%
0.0521796
 
1.9%
0.1820496
 
1.8%
0.0620488
 
1.8%
0.1920486
 
1.8%
0.220453
 
1.8%
Other values (553)794888
71.1%
ValueCountFrequency (%)
070153
6.3%
0.0162703
5.6%
0.0235547
3.2%
0.0327544
 
2.5%
0.0423568
 
2.1%
0.0521796
 
1.9%
0.0620488
 
1.8%
0.0719701
 
1.8%
0.0819521
 
1.7%
0.0919406
 
1.7%
ValueCountFrequency (%)
8.461
< 0.1%
7.981
< 0.1%
7.91
< 0.1%
7.311
< 0.1%
71
< 0.1%
6.861
< 0.1%
6.61
< 0.1%
6.551
< 0.1%
6.531
< 0.1%
6.41
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing48614
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean179.6703195
Minimum0
Maximum360
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:50.320769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.35
Q189.38
median178.995
Q3269.43
95-th percentile329.82
Maximum360
Range360
Interquartile range (IQR)180.05

Descriptive statistics

Standard deviation99.26149134
Coefficient of variation (CV)0.5524646008
Kurtosis-1.369466549
Mean179.6703195
Median Absolute Deviation (MAD)90.015
Skewness0.01038449911
Sum192158844.1
Variance9852.843663
MonotonicityNot monotonic
2022-11-02T12:08:50.442867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901988
 
0.2%
93.34112
 
< 0.1%
82.82106
 
< 0.1%
81.35104
 
< 0.1%
84.84103
 
< 0.1%
93.2103
 
< 0.1%
89.76103
 
< 0.1%
82.68102
 
< 0.1%
266.07102
 
< 0.1%
84.26101
 
< 0.1%
Other values (35991)1066584
95.4%
(Missing)48614
 
4.3%
ValueCountFrequency (%)
010
< 0.1%
0.0120
< 0.1%
0.0210
< 0.1%
0.0321
< 0.1%
0.0419
< 0.1%
0.0518
< 0.1%
0.0611
< 0.1%
0.0717
< 0.1%
0.0818
< 0.1%
0.0921
< 0.1%
ValueCountFrequency (%)
36013
< 0.1%
359.9918
< 0.1%
359.9824
< 0.1%
359.9713
< 0.1%
359.9613
< 0.1%
359.9518
< 0.1%
359.9413
< 0.1%
359.9320
< 0.1%
359.9219
< 0.1%
359.9113
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing48614
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.8124916
Minimum0
Maximum360
Zeros56
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.5 MiB
2022-11-02T12:08:50.574224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.08
Q191.03
median179.87
Q3270.92
95-th percentile337.19
Maximum360
Range360
Interquartile range (IQR)179.89

Descriptive statistics

Standard deviation101.1737089
Coefficient of variation (CV)0.5595504378
Kurtosis-1.290945778
Mean180.8124916
Median Absolute Deviation (MAD)89.93
Skewness-7.171900654 × 10-6
Sum193380406.3
Variance10236.11936
MonotonicityNot monotonic
2022-11-02T12:08:50.699216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.0175
 
< 0.1%
277.6774
 
< 0.1%
8973
 
< 0.1%
266.2972
 
< 0.1%
93.5571
 
< 0.1%
95.1471
 
< 0.1%
268.9471
 
< 0.1%
275.1271
 
< 0.1%
96.8670
 
< 0.1%
263.5170
 
< 0.1%
Other values (35991)1068790
95.6%
(Missing)48614
 
4.3%
ValueCountFrequency (%)
056
< 0.1%
0.0120
 
< 0.1%
0.0231
< 0.1%
0.0328
< 0.1%
0.0423
< 0.1%
0.0522
 
< 0.1%
0.0620
 
< 0.1%
0.0726
< 0.1%
0.0824
< 0.1%
0.0921
 
< 0.1%
ValueCountFrequency (%)
36013
< 0.1%
359.9918
< 0.1%
359.9821
< 0.1%
359.9725
< 0.1%
359.9629
< 0.1%
359.9518
< 0.1%
359.9428
< 0.1%
359.9331
< 0.1%
359.9222
< 0.1%
359.9118
< 0.1%

event
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
None
1028537 
ball_snap
 
26956
pass_forward
 
24127
autoevent_ballsnap
 
13455
autoevent_passforward
 
12627
Other values (18)
 
12420

Length

Max length25
Median length4
Mean length4.72758876
Min length3

Characters and Unicode

Total characters5286021
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None1028537
92.0%
ball_snap26956
 
2.4%
pass_forward24127
 
2.2%
autoevent_ballsnap13455
 
1.2%
autoevent_passforward12627
 
1.1%
play_action5382
 
0.5%
qb_sack1334
 
0.1%
run1219
 
0.1%
pass_arrived1150
 
0.1%
autoevent_passinterrupted690
 
0.1%
Other values (13)2645
 
0.2%

Length

2022-11-02T12:08:50.825446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none1028537
92.0%
ball_snap26956
 
2.4%
pass_forward24127
 
2.2%
autoevent_ballsnap13455
 
1.2%
autoevent_passforward12627
 
1.1%
play_action5382
 
0.5%
qb_sack1334
 
0.1%
run1219
 
0.1%
pass_arrived1150
 
0.1%
autoevent_passinterrupted690
 
0.1%
Other values (13)2645
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1105794
20.9%
o1099515
20.8%
e1086796
20.6%
N1028537
19.5%
a197869
 
3.7%
s121578
 
2.3%
_88872
 
1.7%
l87032
 
1.6%
p86503
 
1.6%
r78913
 
1.5%
Other values (15)304612
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4168612
78.9%
Uppercase Letter1028537
 
19.5%
Connector Punctuation88872
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1105794
26.5%
o1099515
26.4%
e1086796
26.1%
a197869
 
4.7%
s121578
 
2.9%
l87032
 
2.1%
p86503
 
2.1%
r78913
 
1.9%
t63503
 
1.5%
b42021
 
1.0%
Other values (13)199088
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
N1028537
100.0%
Connector Punctuation
ValueCountFrequency (%)
_88872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5197149
98.3%
Common88872
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1105794
21.3%
o1099515
21.2%
e1086796
20.9%
N1028537
19.8%
a197869
 
3.8%
s121578
 
2.3%
l87032
 
1.7%
p86503
 
1.7%
r78913
 
1.5%
t63503
 
1.2%
Other values (14)241109
 
4.6%
Common
ValueCountFrequency (%)
_88872
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5286021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1105794
20.9%
o1099515
20.8%
e1086796
20.6%
N1028537
19.5%
a197869
 
3.7%
s121578
 
2.3%
_88872
 
1.7%
l87032
 
1.6%
p86503
 
1.6%
r78913
 
1.5%
Other values (15)304612
 
5.8%

Interactions

2022-11-02T12:08:38.813480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:01.244624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:04.768589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:08.103090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:11.636410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:14.984171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:18.324722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:21.817311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:25.132479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:28.486183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:32.031166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:35.358671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:39.270815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:01.533574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:05.048954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:08.395434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:11.926428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:15.268577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:18.607694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:22.097444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:25.416963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:28.766905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:32.311934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:35.653161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:39.564152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:01.815810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:05.321873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:08.667248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:12.203515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:15.548945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:18.880217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:22.370788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:25.701598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:29.051036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:32.589121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:35.938922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:39.853119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:02.097624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:05.613148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:08.947868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:12.476704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:15.828084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:19.165591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:22.643587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:25.978116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:29.330329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:32.861330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:36.227125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:40.144521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:02.523743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:05.899532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:09.229288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:12.759837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:16.102652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:19.443577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:22.921492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:26.269448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:29.623640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:33.145573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:36.519721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:40.437268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:02.803466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:06.170458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:09.505526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:13.035131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:16.379993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:19.710408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:23.192924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:26.546539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:30.062322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:33.416297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:36.805234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:40.735116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:03.082991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:06.443043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:09.782897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:13.311060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:16.656198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:19.988737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:23.462539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:26.828247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:30.346303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:33.689523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:37.091720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:41.031353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:03.367667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:06.720137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:10.066485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:13.590087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:16.944712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:20.263296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:23.735342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:27.099519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:30.622652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:33.963005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:37.374280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:41.320443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:03.645058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:06.992605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:10.348648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:13.867533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:17.222789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:20.707008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:24.015936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:27.373538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:30.896117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:34.236048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:37.662771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:41.610984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:03.917833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:07.264943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:10.630737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:14.140962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:17.499646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:20.980850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:24.296480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:27.645706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:31.170788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:34.505462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:37.952329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:41.896208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:04.199539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:07.540960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:10.910964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:14.418691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:17.770585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:21.253933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:24.566880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:27.924632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:31.453686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:34.787905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:38.231404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:42.184469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:04.488493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:07.825874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:11.199490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:14.704316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:18.051816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:21.535013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:24.850532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:28.212593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:31.753956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:35.075122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:08:38.521844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T12:08:51.082284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T12:08:51.233880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T12:08:51.379414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T12:08:51.525708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T12:08:51.660281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T12:08:51.769248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T12:08:42.769845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T12:08:44.040095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T12:08:45.887578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T12:08:46.571112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020210909009725511.012021-09-10T00:26:31.10012.0TBright37.7724.220.290.300.03165.1684.99None
120210909009725511.022021-09-10T00:26:31.20012.0TBright37.7824.220.230.110.02164.3392.87None
220210909009725511.032021-09-10T00:26:31.30012.0TBright37.7824.240.160.100.01160.2468.55None
320210909009725511.042021-09-10T00:26:31.40012.0TBright37.7324.250.150.240.06152.13296.85None
420210909009725511.052021-09-10T00:26:31.50012.0TBright37.6924.260.250.180.04148.33287.55None
520210909009725511.062021-09-10T00:26:31.60012.0TBright37.6424.260.350.530.05144.42282.72ball_snap
620210909009725511.072021-09-10T00:26:31.70012.0TBright37.5624.260.541.050.08137.49272.95None
720210909009725511.082021-09-10T00:26:31.80012.0TBright37.4724.250.801.850.09131.95267.49None
820210909009725511.092021-09-10T00:26:31.90012.0TBright37.3824.240.992.030.09129.85263.48None
920210909009725511.0102021-09-10T00:26:32.00012.0TBright37.2724.231.191.820.11123.79263.77None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
111811220210913004845NaN252021-09-14T03:54:20.100NaNfootballleft50.8424.734.300.460.46NaNNaNNone
111811320210913004845NaN262021-09-14T03:54:20.200NaNfootballleft51.2524.834.251.150.43NaNNaNNone
111811420210913004845NaN272021-09-14T03:54:20.300NaNfootballleft51.6724.934.141.830.42NaNNaNNone
111811520210913004845NaN282021-09-14T03:54:20.400NaNfootballleft52.0625.033.961.930.40NaNNaNNone
111811620210913004845NaN292021-09-14T03:54:20.500NaNfootballleft52.4325.133.771.980.39NaNNaNautoevent_passforward
111811720210913004845NaN302021-09-14T03:54:20.600NaNfootballleft52.7825.233.581.950.37NaNNaNpass_forward
111811820210913004845NaN312021-09-14T03:54:20.700NaNfootballleft50.3126.4617.160.252.77NaNNaNNone
111811920210913004845NaN322021-09-14T03:54:20.800NaNfootballleft48.6626.9917.101.051.73NaNNaNNone
111812020210913004845NaN332021-09-14T03:54:20.900NaNfootballleft47.0427.5316.981.671.71NaNNaNNone
111812120210913004845NaN342021-09-14T03:54:21.000NaNfootballleft45.4228.0816.891.821.71NaNNaNNone